Title:AirSim Drone Racing Lab

Abstract: Autonomous drone racing is a challenging research problem at the intersection
of computer vision, planning, state estimation, and control. We introduce
AirSim Drone Racing Lab, a simulation framework for enabling fast prototyping
of algorithms for autonomy and enabling machine learning research in this
domain, with the goal of reducing the time, money, and risks associated with
field robotics. Our framework enables generation of racing tracks in multiple
photo-realistic environments, orchestration of drone races, comes with a suite
of gate assets, allows for multiple sensor modalities (monocular, depth,
neuromorphic events, optical flow), different camera models, and benchmarking
of planning, control, computer vision, and learning-based algorithms. We used
our framework to host a simulation based drone racing competition at NeurIPS
2019. The competition binaries are available at our github repository.